Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Understand

Our paper deals with inferring simulator-based statistical models given some observed data.

• A simulator-based model is a parametrized mechanism which specifies how data are generated.
• It is thus also referred to as generative model.
• We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables.